Incremental Principal Component Analysis-based Sparse Representation for Face Pose Classification

Yuyao Zhang (1) , Younes Houari Benhamza (1) , Khalid Idrissi (1) , Christophe Garcia (1)

1 imagine - Extraction de Caractéristiques et Identification

This paper proposes an Adaptive Sparse Representation pose Classification (ASRC) algorithm to deal with face pose estimation in occlusion, bad illumination and low-resolution cases. The proposed approach classifes different poses, the appearance of face images from the same pose being modelled by an online eigenspace which is built via Incremental Principal Component Analysis. Then the combination of the eigenspaces of all pose classes are used as an over-complete dictionary for sparse representation and classification. However, the big amount of training images may lead to build an extremely large dictionary which will decelerate the classification procedure. To avoid this situation, we devise a conditional update method that updates the training eigenspace only with the misclassified face images. Experimental results show that the proposed method is very robust when the illumination condition changes very dynamically and image resolutions are quite poor.

Domaines: Informatique [cs]

Yuyao Zhang, Younes Houari Benhamza, Khalid Idrissi, Christophe Garcia. Incremental Principal Component Analysis-based Sparse Representation for Face Pose Classification. International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2013), Oct 2013, Poznan, Poland. pp. 620-631, ⟨10.1007/978-3-319-02895-8_56⟩. ⟨hal-01351703⟩